LAION · Complete annotated recording · CC BY 4.0
One performance.
Every annotation, explained.
German-English Voice Acting Challenges · 50-audio comparison · Exact grammar
The Last Sunset on Our Beach
de-00001 · DE · generation voice Charon · 33.74 seconds
Original media bytes are retained. This self-contained page also works offline.
MOSS whole-audio description
A single young-adult to mature-adult masculine speaker delivers an intimate, deeply contemplative monologue in Standard German. The vocal production is exceptionally quiet, soft-spoken, and breathy, hovering between a close-mic whisper and a gentle half-voice. The tempo is slow and measured, broken into short, reflective phrases separated by prominent, audible inhalations and weary sighs. The timbre is warm and rounded with low-mid chest resonance, low tension, and minimal metallic edge, creating an ASMR-adjacent, confessional atmosphere. The emotional tone is dominated by wistful longing, gentle sadness, and quiet vulnerability, with a subtle undercurrent of helplessness as the speaker reflects on the passing of time. The recording is pristine, dry, and close, with virtually no background noise, allowing the delicate breath dynamics and subtle vocal fry to remain fully audible and organically integrated.
Predicted vocal events
Threshold 0.6, 0.08-s minimum duration, overlap suppression at half the shorter interval, no event-count cap. Type probabilities are conditional class softmax, not calibrated detection confidence. Click an interval to seek.
Duration 1.14 s · onset probability 0.753
Breath: 60.0% · Exasperated Sigh: 9.3% · Sharp Inhale: 7.7%
A slow, audible, breathy inhalation and soft exhalation occurs in the pause between 'glänzt' and 'Fast genau wie damals', conveying quiet contemplation and emotional weight.
Duration 0.74 s · onset probability 0.748
Breath: 28.8% · Exasperated Sigh: 20.9% · Sharp Inhale: 9.9%
A slow, audible inhalation and soft exhalation occurs in the pause after 'Fast genau wie damals', reflecting quiet contemplation and emotional grounding.
Duration 0.96 s · onset probability 0.775
Breath: 50.2% · Exasperated Sigh: 19.0% · Sharp Inhale: 4.8%
A slow, audible breath intake and release during a pause, reflecting quiet contemplation and emotional weight.
Duration 1.06 s · onset probability 0.828
Breath: 32.0% · Exasperated Sigh: 23.4% · Exhausted Groan: 7.4%
A slow, audible breath intake and release between 'deine Hand' and 'sie fühlt so kühl', emphasizing tactile intimacy and emotional vulnerability.
Duration 1.10 s · onset probability 0.702
Breath: 38.6% · Exasperated Sigh: 19.8% · Wistful Sigh: 12.3%
A slow, audible breath intake and release occurs in the pause before 'Sie fühlt so kühl', conveying physical closeness and emotional vulnerability.
Duration 1.10 s · onset probability 0.882
Exasperated Sigh: 57.7% · Exhausted Groan: 7.3% · Breath: 5.0%
A soft, tremulous exhalation and quiet sob-like release following 'untergeht', conveying deep emotional helplessness and sorrow.
Procedural GENERAL and SCRIPT
Threshold 0.7; two distinctive voice properties, up to one emotion, compact tags. This independently selected event list can differ from MOSS. Content Enjoyment supplies pleasantness wording.
GENERAL: very whispered, very noisy, very slow, very burnout; vocal-burst candidates are marked in the timed event list SCRIPT: (very denasal, very full, awestruck, enjoyable) [0.47 seconds duration] Schau [0.25 seconds pause] (very denasal, very full, awestruck, enjoyable) [0.06 seconds duration] mal, [0.50 seconds pause] (very denasal, very full, awestruck, enjoyable) [1.60 seconds duration] wie golden das Wasser glänzt. [0.84 seconds pause] (Breath, 1.14 seconds) (very naturalistic, very denasal, wistfulness) [2.96 seconds duration] Fast genau wie damals. (Vocal Burst, 0.74 seconds) (Breath, 0.96 seconds) [0.50 seconds pause] (very naturalistic, very organic, slightly contemplation) [1.83 seconds duration] Der Sand unter uns ist noch ganz warm. [1.61 seconds pause] (very whispered, very organic, very lethargy) [1.06 seconds duration] Aber deine Hand, (very whispered, very organic, very lethargy) [2.83 seconds duration] (Vocal Burst, 1.06 seconds) (Vocal Burst, 1.10 seconds) sie wird so kühl. [2.03 seconds pause] (very denasal, very naturalistic, very despair, enjoyable) [3.32 seconds duration] Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht. [0.92 seconds pause] (very steady, very full, slightly nostalgia) [1.74 seconds duration] Wie Zeit, die uns wegrutscht. [0.74 seconds pause] (very fading, very naturalistic, very apathy) [2.92 seconds duration] Ich will nicht, dass die Sonne untergeht. (Exasperated Sigh, 1.10 seconds) [1.48 seconds pause] (very fading, very flat, very detachment, less-enjoyable) [0.38 seconds duration] Bitte. [2.10 seconds pause] (very denasal, hi-fi, slightly zeal) [1.71 seconds duration] Ich bin doch überhaupt nicht bereit dafür. [0.75 seconds pause]
All ASR words are retained exactly once. Every selected burst and speech segment has a numeric duration. Inline overlapping events are included in the enclosing segment's time budget. Verification checks serialization; it does not establish acoustic accuracy.
Choose a Humaneness Voice training representation
Two caption choices × three transcript choices. The no-appended-event caption retains the original whole-audio description, which can itself mention vocal events. Plain transcript is verbatim ASR; timed variants use the MOSS intervals. Original intended generation text is not substituted for ASR.
All 192 regression targets, plus CPS and count
Raw values remain on the original target scales. Bars and ranking use reference-relative z-scores from the preserved 118,692-clip mixed reference; the procedural writer uses a separate 80,265-clip speech reference. z is (prediction − mean) / population standard deviation, not a probability. Bars are clipped at ±3 only for display.
40 emotion predictions
| Target | Raw | z | −3 · 0 · +3 |
|---|---|---|---|
| Fatigue Exhaustion emo_Fatigue_Exhaustion | 2.615 | +1.56 | |
| Sadness emo_Sadness | 2.047 | +1.48 | |
| Contemplation emo_Contemplation | 2.797 | +1.46 | |
| Helplessness emo_Helplessness | 2.328 | +1.24 | |
| Disappointment emo_Disappointment | 1.725 | +1.18 | |
| Doubt emo_Doubt | 1.583 | +1.16 | |
| Contentment emo_Contentment | 0.165 | -1.14 | |
| Distress emo_Distress | 2.215 | +1.01 | |
| Longing emo_Longing | 1.005 | +1.00 | |
| Hope Enthusiasm Optimism emo_Hope_Enthusiasm_Optimism | 0.178 | -0.94 | |
| Pride emo_Pride | 0.012 | -0.89 | |
| Interest emo_Interest | 1.128 | -0.83 | |
| Pain emo_Pain | 0.969 | +0.79 | |
| Emotional Numbness emo_Emotional_Numbness | 0.492 | +0.75 | |
| Amusement emo_Amusement | 0.041 | -0.75 | |
| Elation emo_Elation | -0.084 | -0.63 | |
| Teasing emo_Teasing | -0.024 | -0.63 | |
| Triumph emo_Triumph | -0.055 | -0.57 | |
| Astonishment Surprise emo_Astonishment_Surprise | 0.049 | -0.56 | |
| Embarrassment emo_Embarrassment | 0.013 | -0.45 | |
| Relief emo_Relief | 0.097 | -0.42 | |
| Confusion emo_Confusion | 0.645 | +0.40 | |
| Pleasure Ecstasy emo_Pleasure_Ecstasy | 0.004 | -0.38 | |
| Infatuation emo_Infatuation | -0.030 | -0.38 | |
| Fear emo_Fear | 0.673 | +0.38 | |
| Shame emo_Shame | 0.019 | -0.31 | |
| Sexual Lust emo_Sexual_Lust | -0.014 | -0.28 | |
| Thankfulness Gratitude emo_Thankfulness_Gratitude | 0.056 | -0.26 | |
| Anger emo_Anger | 0.166 | -0.21 | |
| Affection emo_Affection | 0.317 | -0.20 | |
| Disgust emo_Disgust | 0.056 | -0.19 | |
| Contempt emo_Contempt | 0.067 | -0.18 | |
| Impatience and Irritability emo_Impatience_and_Irritability | 0.381 | -0.17 | |
| Bitterness emo_Bitterness | 0.396 | +0.14 | |
| Malevolence Malice emo_Malevolence_Malice | 0.007 | -0.14 | |
| Intoxication Altered States of Consciousness emo_Intoxication_Altered_States_of_Consciousness | 0.020 | -0.11 | |
| Sourness emo_Sourness | 0.236 | -0.11 | |
| Awe emo_Awe | 0.236 | +0.10 | |
| Jealousy and Envy emo_Jealousy_and_Envy | 0.014 | +0.07 | |
| Concentration emo_Concentration | 1.647 | -0.03 |
57 VoiceNet dimensions
| Target | Raw | z | −3 · 0 · +3 |
|---|---|---|---|
| VoiceNet R_NASL vn_R_NASL_reg | 0.771 | -2.47 | |
| VoiceNet DARC vn_DARC_reg | 2.055 | -2.03 | |
| VoiceNet VFLX vn_VFLX_reg | 2.317 | -2.03 | |
| VoiceNet R_ORAL vn_R_ORAL_reg | 2.564 | -1.98 | |
| VoiceNet R_MASK vn_R_MASK_reg | 1.558 | -1.94 | |
| VoiceNet RANG vn_RANG_reg | 1.624 | -1.87 | |
| VoiceNet HARM vn_HARM_reg | 1.783 | -1.83 | |
| VoiceNet S_WHIS vn_S_WHIS_reg | 2.854 | +1.76 | |
| VoiceNet METL vn_METL_reg | 0.779 | -1.67 | |
| VoiceNet S_ASMR vn_S_ASMR_reg | 2.583 | +1.66 | |
| VoiceNet R_HEAD vn_R_HEAD_reg | 0.943 | -1.54 | |
| VoiceNet ATCK vn_ATCK_reg | 1.835 | -1.52 | |
| VoiceNet S_PLAY vn_S_PLAY_reg | -0.189 | -1.50 | |
| VoiceNet BRGT vn_BRGT_reg | 2.459 | -1.47 | |
| VoiceNet TEMP vn_TEMP_reg | 1.808 | -1.43 | |
| VoiceNet STNC vn_STNC_reg | 1.262 | -1.43 | |
| VoiceNet AROU vn_AROU_reg | 1.634 | -1.42 | |
| VoiceNet R_MIXD vn_R_MIXD_reg | 2.599 | -1.38 | |
| VoiceNet S_MONO vn_S_MONO_reg | 3.904 | +1.29 | |
| VoiceNet ARSH vn_ARSH_reg | 2.522 | -1.18 | |
| VoiceNet RESP vn_RESP_reg | 3.856 | +1.13 | |
| VoiceNet VALN vn_VALN_reg | 1.579 | -1.09 | |
| VoiceNet ROUG vn_ROUG_reg | 2.848 | +1.09 | |
| VoiceNet VULN vn_VULN_reg | 4.183 | +0.98 | |
| VoiceNet EMPH vn_EMPH_reg | 2.590 | -0.97 | |
| VoiceNet SMTH vn_SMTH_reg | 2.369 | -0.96 | |
| VoiceNet S_AUTH vn_S_AUTH_reg | 0.833 | -0.96 | |
| VoiceNet CHNK vn_CHNK_reg | 1.804 | -0.96 | |
| VoiceNet CLRT vn_CLRT_reg | 3.062 | -0.94 | |
| VoiceNet S_CART vn_S_CART_reg | -0.011 | -0.93 | |
| VoiceNet REGS vn_REGS_reg | 1.159 | -0.90 | |
| VoiceNet FOCS vn_FOCS_reg | 3.394 | -0.89 | |
| VoiceNet S_RANT vn_S_RANT_reg | 0.158 | -0.78 | |
| VoiceNet S_TECH vn_S_TECH_reg | 0.654 | -0.77 | |
| VoiceNet R_THRT vn_R_THRT_reg | 3.097 | +0.76 | |
| VoiceNet S_CONV vn_S_CONV_reg | 2.604 | -0.76 | |
| VoiceNet GEND vn_GEND_reg | 4.628 | +0.73 | |
| VoiceNet ESTH vn_ESTH_reg | 3.198 | -0.70 | |
| VoiceNet TENS vn_TENS_reg | 2.989 | +0.61 | |
| VoiceNet R_CHST vn_R_CHST_reg | 3.321 | +0.53 | |
| VoiceNet S_NEWS vn_S_NEWS_reg | 0.245 | -0.52 | |
| VoiceNet VALS vn_VALS_reg | 2.524 | -0.50 | |
| VoiceNet STRU vn_STRU_reg | 3.104 | -0.49 | |
| VoiceNet S_DRAM vn_S_DRAM_reg | 3.022 | +0.45 | |
| VoiceNet COGL vn_COGL_reg | 2.290 | +0.42 | |
| VoiceNet BKGN vn_BKGN_reg | 3.565 | -0.36 | |
| VoiceNet S_FORM vn_S_FORM_reg | 1.344 | -0.29 | |
| VoiceNet S_CASU vn_S_CASU_reg | 2.975 | -0.28 | |
| VoiceNet WARM vn_WARM_reg | 3.300 | +0.25 | |
| VoiceNet DFLU vn_DFLU_reg | 1.747 | +0.25 | |
| VoiceNet AGEV vn_AGEV_reg | 3.191 | +0.19 | |
| VoiceNet EXPL vn_EXPL_reg | 0.058 | +0.18 | |
| VoiceNet RCQL vn_RCQL_reg | 3.817 | +0.18 | |
| VoiceNet S_NARR vn_S_NARR_reg | 1.949 | -0.17 | |
| VoiceNet VOLT vn_VOLT_reg | 1.970 | -0.10 | |
| VoiceNet FULL vn_FULL_reg | 3.207 | +0.08 | |
| VoiceNet S_STRY vn_S_STRY_reg | 2.122 | -0.03 |
Quality, enjoyment, genuineness, blend, CPS and remaining targets
| Target | Raw | z | −3 · 0 · +3 |
|---|---|---|---|
| estimated burst count estimated_burst_count | 32.000 | +2.19 | |
| voiceclap attribute:Fatigue Exhaustion voiceclap_attribute:Fatigue_Exhaustion | 2.184 | +1.75 | |
| burst count log1p burst_count_log1p | 1.902 | +1.42 | |
| voiceclap attribute:Helplessness voiceclap_attribute:Helplessness | 2.099 | +1.41 | |
| voiceclap attribute:Pain voiceclap_attribute:Pain | 1.099 | +1.41 | |
| voiceclap attribute:Sadness voiceclap_attribute:Sadness | 1.550 | +1.40 | |
| voiceclap attribute:duration voiceclap_attribute:duration | 24.253 | +1.36 | |
| voiceclap attribute:Interest voiceclap_attribute:Interest | 0.638 | -1.35 | |
| voiceclap attribute:Intoxication Altered States of Consciousness voiceclap_attribute:Intoxication_Altered_States_of_Consciousness | 0.203 | +1.32 | |
| voiceclap attribute:Longing voiceclap_attribute:Longing | 0.645 | +1.29 | |
| voiceclap attribute:Distress voiceclap_attribute:Distress | 2.408 | +1.26 | |
| voiceclap attribute:Disappointment voiceclap_attribute:Disappointment | 1.553 | +1.22 | |
| voiceclap attribute:Fear voiceclap_attribute:Fear | 0.689 | +1.15 | |
| voiceclap attribute:Hope Enthusiasm Optimism voiceclap_attribute:Hope_Enthusiasm_Optimism | 0.083 | -1.10 | |
| voiceclap attribute:Contentment voiceclap_attribute:Contentment | 0.304 | -1.06 | |
| voiceclap attribute:Shame voiceclap_attribute:Shame | 0.237 | +1.03 | |
| voiceclap attribute:Teasing voiceclap_attribute:Teasing | -0.139 | -1.03 | |
| voiceclap attribute:Amusement voiceclap_attribute:Amusement | -0.130 | -1.03 | |
| voiceclap attribute:Confident vs. Hesitant voiceclap_attribute:Confident_vs._Hesitant | 0.992 | -0.96 | |
| voiceclap attribute:Emotional Numbness voiceclap_attribute:Emotional_Numbness | 0.272 | +0.96 | |
| voiceclap attribute:score speech quality voiceclap_attribute:score_speech_quality | 1.872 | -0.95 | |
| eiv extra:score speech quality eiv_extra:score_speech_quality | 1.778 | -0.93 | |
| voiceclap attribute:Background Noise voiceclap_attribute:Background_Noise | 0.664 | +0.91 | |
| voiceclap attribute:Bitterness voiceclap_attribute:Bitterness | 0.666 | +0.90 | |
| voiceclap attribute:Contempt voiceclap_attribute:Contempt | -0.156 | -0.88 | |
| voiceclap attribute:Vulnerable vs. Emotionally Detached voiceclap_attribute:Vulnerable_vs._Emotionally_Detached | 0.901 | -0.81 | |
| voiceclap attribute:talking speed voiceclap_attribute:talking_speed | 8.696 | -0.78 | |
| voiceclap attribute:Elation voiceclap_attribute:Elation | -0.131 | -0.77 | |
| voiceclap attribute:Astonishment Surprise voiceclap_attribute:Astonishment_Surprise | 0.045 | -0.77 | |
| voiceclap attribute:Doubt voiceclap_attribute:Doubt | 0.928 | +0.76 | |
| voiceclap attribute:score background quality voiceclap_attribute:score_background_quality | 3.661 | +0.73 | |
| voiceclap attribute:Pride voiceclap_attribute:Pride | 0.068 | -0.72 | |
| voiceclap attribute:Triumph voiceclap_attribute:Triumph | -0.068 | -0.67 | |
| voiceclap attribute:Concentration voiceclap_attribute:Concentration | 1.166 | -0.67 | |
| eiv extra:Confident vs. Hesitant eiv_extra:Confident_vs._Hesitant | 0.953 | -0.66 | |
| voiceclap attribute:Warm vs. Cold voiceclap_attribute:Warm_vs._Cold | -0.135 | -0.65 | |
| eiv extra:Monotone vs. Expressive eiv_extra:Monotone_vs._Expressive | 1.512 | -0.65 | |
| voiceclap attribute:Pleasure Ecstasy voiceclap_attribute:Pleasure_Ecstasy | -0.077 | -0.63 | |
| voiceclap attribute:Valence voiceclap_attribute:Valence | -0.571 | -0.62 | |
| voiceclap attribute:Serious vs. Humorous voiceclap_attribute:Serious_vs._Humorous | 0.686 | -0.61 | |
| audiobox:PQ audiobox:PQ | 7.504 | +0.58 | |
| voiceclap attribute:Sexual Lust voiceclap_attribute:Sexual_Lust | 0.072 | +0.57 | |
| voiceclap attribute:Jealousy & Envy voiceclap_attribute:Jealousy_&_Envy | 0.069 | +0.57 | |
| voiceclap attribute:Arousal voiceclap_attribute:Arousal | 1.307 | -0.55 | |
| characters per second characters_per_second | 9.891 | -0.55 | |
| genuineness 0 6 genuineness_0_6 | 4.093 | -0.54 | |
| eiv extra:Gender eiv_extra:Gender | -0.354 | -0.53 | |
| eiv extra:score content enjoyment eiv_extra:score_content_enjoyment | 4.826 | +0.52 | |
| voiceclap attribute:score content enjoyment voiceclap_attribute:score_content_enjoyment | 4.865 | +0.51 | |
| eiv extra:Submissive vs. Dominant eiv_extra:Submissive_vs._Dominant | 0.562 | +0.50 | |
| eiv extra:score background quality eiv_extra:score_background_quality | 3.662 | +0.50 | |
| voiceclap attribute:Relief voiceclap_attribute:Relief | 0.428 | +0.50 | |
| voiceclap attribute:score overall quality voiceclap_attribute:score_overall_quality | 2.906 | +0.48 | |
| voiceclap attribute:Malevolence Malice voiceclap_attribute:Malevolence_Malice | -0.043 | -0.48 | |
| eiv extra:High-Pitched vs. Low-Pitched eiv_extra:High-Pitched_vs._Low-Pitched | 1.816 | +0.47 | |
| dnsmos:BAK dnsmos:BAK | 3.970 | +0.47 | |
| eiv extra:score overall quality eiv_extra:score_overall_quality | 2.958 | +0.47 | |
| eiv extra:Valence eiv_extra:Valence | 0.015 | -0.46 | |
| dnsmos:BAK raw dnsmos:BAK_raw | 4.094 | +0.45 | |
| voiceclap attribute:Monotone vs. Expressive voiceclap_attribute:Monotone_vs._Expressive | 1.656 | -0.44 | |
| voiceclap attribute:Contemplation voiceclap_attribute:Contemplation | 1.791 | +0.41 | |
| voiceclap attribute:Sourness voiceclap_attribute:Sourness | 0.409 | +0.39 | |
| voiceclap attribute:Disgust voiceclap_attribute:Disgust | 0.010 | -0.36 | |
| voiceclap attribute:Recording Quality voiceclap_attribute:Recording_Quality | 2.325 | -0.32 | |
| voiceclap attribute:Infatuation voiceclap_attribute:Infatuation | 0.070 | +0.31 | |
| eiv extra:Authenticity eiv_extra:Authenticity | 3.029 | +0.31 | |
| eiv extra:Recording Quality eiv_extra:Recording_Quality | 2.362 | -0.30 | |
| audiobox:PC audiobox:PC | 1.751 | +0.29 | |
| voiceclap attribute:Age voiceclap_attribute:Age | 3.160 | +0.29 | |
| voiceclap attribute:Awe voiceclap_attribute:Awe | 0.091 | -0.28 | |
| eiv extra:Background Noise eiv_extra:Background_Noise | 0.492 | +0.26 | |
| eiv extra:Vulnerable vs. Emotionally Detached eiv_extra:Vulnerable_vs._Emotionally_Detached | 1.177 | -0.24 | |
| eiv extra:Arousal eiv_extra:Arousal | 1.543 | -0.22 | |
| voiceclap attribute:Submissive vs. Dominant voiceclap_attribute:Submissive_vs._Dominant | 0.509 | +0.22 | |
| voiceclap attribute:High-Pitched vs. Low-Pitched voiceclap_attribute:High-Pitched_vs._Low-Pitched | 1.708 | +0.21 | |
| voiceclap attribute:Thankfulness Gratitude voiceclap_attribute:Thankfulness_Gratitude | 0.234 | +0.18 | |
| dnsmos:OVRL raw dnsmos:OVRL_raw | 3.468 | +0.18 | |
| eiv extra:Serious vs. Humorous eiv_extra:Serious_vs._Humorous | 0.923 | -0.17 | |
| dnsmos:OVRL dnsmos:OVRL | 3.071 | +0.17 | |
| voiceclap attribute:Impatience and Irritability voiceclap_attribute:Impatience_and_Irritability | 0.532 | +0.17 | |
| voiceclap attribute:Gender voiceclap_attribute:Gender | 0.128 | -0.14 | |
| voiceclap attribute:Anger voiceclap_attribute:Anger | 0.200 | -0.13 | |
| audiobox:CE audiobox:CE | 5.318 | -0.13 | |
| eiv extra:Warm vs. Cold eiv_extra:Warm_vs._Cold | 0.194 | -0.12 | |
| voiceclap attribute:Confusion voiceclap_attribute:Confusion | 0.357 | +0.08 | |
| voiceclap attribute:Affection voiceclap_attribute:Affection | 0.411 | +0.08 | |
| voiceclap attribute:Soft vs. Harsh voiceclap_attribute:Soft_vs._Harsh | 0.396 | +0.07 | |
| dnsmos:P808 MOS dnsmos:P808_MOS | 3.590 | -0.07 | |
| dnsmos:SIG raw dnsmos:SIG_raw | 3.697 | -0.06 | |
| dnsmos:SIG dnsmos:SIG | 3.338 | -0.06 | |
| eiv extra:Soft vs. Harsh eiv_extra:Soft_vs._Harsh | 0.377 | -0.06 | |
| eiv extra:Age eiv_extra:Age | 3.136 | +0.05 | |
| voiceclap attribute:Authenticity voiceclap_attribute:Authenticity | 2.979 | +0.05 | |
| voiceclap attribute:Embarrassment voiceclap_attribute:Embarrassment | 0.082 | -0.04 | |
| audiobox:CU audiobox:CU | 6.175 | +0.04 | |
| blend 0 10 blend_0_10 | 7.740 | +0.03 | |
| R quality R_quality | 0.832 | +0.01 |
Predicted Orange embeddings: 128D timbre and 250D identity
These student predictions approximate the original embedding teachers; they do not establish a person's actual identity.
{
"orange_timbre_128d": [
-0.04983147606253624,
0.023882592096924782,
-0.10610577464103699,
-0.0997358113527298,
0.008297921158373356,
-0.09964712709188461,
-0.03593664616346359,
0.023651329800486565,
0.06697720289230347,
-0.0924522876739502,
0.20571409165859222,
-0.011516612954437733,
0.05164948105812073,
-0.08872520178556442,
-0.07791733741760254,
-0.06398153305053711,
-0.1117740347981453,
-0.07198496907949448,
0.06820324063301086,
-0.08438748866319656,
0.16487917304039001,
0.05484118312597275,
-0.0926118865609169,
-0.04861260578036308,
-0.09429089725017548,
-0.0910421684384346,
-0.029078027233481407,
-0.05176115781068802,
-0.06215202808380127,
0.12419476360082626,
0.1089307963848114,
0.0925208181142807,
-0.0588122233748436,
-0.04414619132876396,
-0.10966892540454865,
0.24869053065776825,
-0.10426044464111328,
0.01955111138522625,
-0.08710084110498428,
-0.10220997780561447,
-0.09901589900255203,
-0.11599769443273544,
-0.06689763069152832,
-0.10077015310525894,
0.24835310876369476,
-0.08648348599672318,
-0.019590899348258972,
-0.12266386300325394,
-0.11799351125955582,
0.0491182766854763,
0.060477349907159805,
-0.02687610127031803,
0.10162234306335449,
0.06204751878976822,
-0.0650954470038414,
0.14747367799282074,
0.09298334270715714,
-0.10431508719921112,
-0.08620548993349075,
0.028218740597367287,
-0.07901781797409058,
0.1483258754014969,
-0.07442032545804977,
0.01107813697308302,
0.048053257167339325,
-0.094272680580616,
0.09073445945978165,
-0.0988701805472374,
0.10603292286396027,
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}All original generation metadata
Exact voice, prompts, model identifiers, source revision and usage records are preserved; credentials are excluded. These intended performance directions were withheld from the annotation models.
{
"key": "de-00001",
"language": "de",
"language_tag": "de-DE",
"voice": "Charon",
"challenge_id": 1,
"source": "existing_challenge_inspired",
"title": "The Last Sunset on Our Beach",
"instruction": "You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.",
"inspired_by": "Relaxing on a Sandy Beach",
"prompt_style_key": "burst",
"prompt_style_label": "Sparse vocal response",
"script": {
"spoken_text": "Schau mal, wie golden das Wasser glänzt... fast genau wie damals. Der Sand unter uns ist noch ganz warm. Aber deine Hand... <breath> sie wird so kühl. Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht. Wie Zeit, die uns wegrutscht. Ich will nicht, dass die Sonne untergeht. <sob> Bitte... ich bin noch überhaupt nicht bereit dafür.",
"beat_styles": [
"tender and quiet",
"trembling realization",
"fragile despair"
],
"intended_emotions": [
"Sadness",
"Grief",
"Love"
],
"scene_note": "A dying evening on the beach turns from comforting reminiscence to raw heartbreak as the speaker realizes their partner's life is slipping away.",
"language_check": {
"method": "langdetect",
"top_language": "de",
"top_probability": 1.0
},
"inline_tags": [
"breath",
"sob"
],
"word_count": 57
},
"script_request": {
"systemInstruction": {
"parts": [
{
"text": "You are a skilled dialogue writer for naturalistic, close-microphone voice acting.\nWrite a short first-person monologue that could be spoken in a real situation. The actor is\nspeaking to another person or themselves, not reciting a plot synopsis. Preserve the\nchallenge's specific situation and emotional development while avoiding melodrama,\ngeneric motivational language, exposition dumps, and perfect studio-script polish.\nHuman emotion is mixed, context-dependent, and gradually changing. Include small,\ncredible imperfections or micro-distractions only when they fit. Spoken text should\nhave about 58-68 words, normally four to six sentences, so that a natural performance\ntakes roughly 26-32 seconds. Use punctuation, ellipses, and occasional exclamation\npoints only where the thought calls for them. Never write stage directions in brackets\nor in the spoken text: Gemini 3.8 TTS will read all ordinary words aloud.\nUse only documented inline tags for momentary human vocal events. No music, foley,\nor background sound tags. Do not overuse vocal tags. Write the spoken dialogue ONLY in natural German (de-DE), not English.\nPreserve the challenge situation rather than translating it literally.\nKeep the inline vocal-event tags in English for TTS. Use 60-75 spoken German words. Keep profanity and graphic content out of the script.\n\nRespond ONLY as a JSON object with keys:\n spoken_text: a single verbatim transcript including optional inline vocal tags;\n beat_styles: an array of one to three brief English delivery directions, in order;\n intended_emotions: one to three of the 40 EmoNet emotion labels if known, otherwise\n concise ordinary emotion words;\n scene_note: one sentence on why the vocal choice fits the challenge.\n"
}
]
},
"contents": [
{
"role": "user",
"parts": [
{
"text": "CHALLENGE TITLE: The Last Sunset on Our Beach\nCHALLENGE INSTRUCTION: You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.\nPROMPT STYLE: Sparse vocal response\nSTYLE-SPECIFIC WRITING DIRECTION: Include one or two credible, situation-specific human non-speech vocal events as inline tags, such as <sigh>, <chuckle>, <gasp>, <groan>, <sob>, <grunt>, <breath>, or <short pause>. Only use an event that a real person in this scene might make; the scene still has coherent spoken language.\nDELIVERY BASELINE: naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis\nWrite all spoken words in natural German (de-DE). Make this a first-person monologue of roughly 30 seconds, with coherent speech and at most two restrained, situation-specific inline vocal-event tags. Choose human imperfections and flowing emotions only when they fit this exact challenge. Do not mention the studio voice or voice name in the dialogue."
}
]
}
],
"generationConfig": {
"responseMimeType": "application/json",
"thinkingConfig": {
"thinkingLevel": "low"
},
"temperature": 0.85
}
},
"script_prompt": "CHALLENGE TITLE: The Last Sunset on Our Beach\nCHALLENGE INSTRUCTION: You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.\nPROMPT STYLE: Sparse vocal response\nSTYLE-SPECIFIC WRITING DIRECTION: Include one or two credible, situation-specific human non-speech vocal events as inline tags, such as <sigh>, <chuckle>, <gasp>, <groan>, <sob>, <grunt>, <breath>, or <short pause>. Only use an event that a real person in this scene might make; the scene still has coherent spoken language.\nDELIVERY BASELINE: naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis\nWrite all spoken words in natural German (de-DE). Make this a first-person monologue of roughly 30 seconds, with coherent speech and at most two restrained, situation-specific inline vocal-event tags. Choose human imperfections and flowing emotions only when they fit this exact challenge. Do not mention the studio voice or voice name in the dialogue.",
"script_system_prompt": "You are a skilled dialogue writer for naturalistic, close-microphone voice acting.\nWrite a short first-person monologue that could be spoken in a real situation. The actor is\nspeaking to another person or themselves, not reciting a plot synopsis. Preserve the\nchallenge's specific situation and emotional development while avoiding melodrama,\ngeneric motivational language, exposition dumps, and perfect studio-script polish.\nHuman emotion is mixed, context-dependent, and gradually changing. Include small,\ncredible imperfections or micro-distractions only when they fit. Spoken text should\nhave about 58-68 words, normally four to six sentences, so that a natural performance\ntakes roughly 26-32 seconds. Use punctuation, ellipses, and occasional exclamation\npoints only where the thought calls for them. Never write stage directions in brackets\nor in the spoken text: Gemini 3.8 TTS will read all ordinary words aloud.\nUse only documented inline tags for momentary human vocal events. No music, foley,\nor background sound tags. Do not overuse vocal tags. Write the spoken dialogue ONLY in natural German (de-DE), not English.\nPreserve the challenge situation rather than translating it literally.\nKeep the inline vocal-event tags in English for TTS. Use 60-75 spoken German words. Keep profanity and graphic content out of the script.\n\nRespond ONLY as a JSON object with keys:\n spoken_text: a single verbatim transcript including optional inline vocal tags;\n beat_styles: an array of one to three brief English delivery directions, in order;\n intended_emotions: one to three of the 40 EmoNet emotion labels if known, otherwise\n concise ordinary emotion words;\n scene_note: one sentence on why the vocal choice fits the challenge.\n",
"script_model": "gemini-3.8-flash",
"script_usage": {
"promptTokenCount": 720,
"candidatesTokenCount": 197,
"totalTokenCount": 917,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 720
}
],
"serviceTier": "SERVICE_TIER_STANDARD"
},
"tts_model": "gemini-3.8-flash-tts",
"tts_request": {
"contents": [
{
"role": "user",
"parts": [
{
"text": "Schau mal, wie golden das Wasser glänzt... fast genau wie damals. Der Sand unter uns ist noch ganz warm. Aber deine Hand... <breath> sie wird so kühl. Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht. Wie Zeit, die uns wegrutscht. Ich will nicht, dass die Sonne untergeht. <sob> Bitte... ich bin noch überhaupt nicht bereit dafür.",
"speech_metadata": {
"style": "naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis; tender and quiet"
}
}
]
}
],
"generationConfig": {
"responseModalities": [
"AUDIO"
],
"speechConfig": {
"voiceConfig": {
"voice": "Charon"
}
}
}
},
"tts_usage": {
"promptTokenCount": 91,
"candidatesTokenCount": 842,
"totalTokenCount": 933,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 91
}
],
"candidatesTokensDetails": [
{
"modality": "AUDIO",
"tokenCount": 842
}
],
"serviceTier": "SERVICE_TIER_STANDARD"
},
"duration_s": 33.68,
"audio_format": "mp3",
"audio_bitrate_kbps": 128,
"mp3_sha256": "3fdef6ec2a5e306994c4da3af5f0e7220b2804f49edf65b244d50c63493bb827",
"voice_assignment_seed": 20261009,
"voice_assignment": "balanced per language and source subset",
"source_repo": "https://github.com/LAION-AI/Voice-Acting-Pipeline",
"source_commit": "e3b0d5281558f7f73e1118c6a9b4c149bb8c0621",
"source_file_sha256": "7ef759b639ef3646ca8a985664747e37ad93e4ab989f97dd8cece4f5201ceb6f"
}Annotation provenance and frozen settings
{
"humaneness_model": "laion/humaneness-ears-base-medium",
"humaneness_revision": "818506970d93c809a3295a0a02c40dee8ff2bfc3",
"caption_models": {
"voice_repo": "laion/moss-voice-captioner",
"voice_revision": "07cad84c33c676ea37979fb7ee844fd06ad92a76",
"burst_repo": "laion/moss-vocal-burst-captioner",
"burst_revision": "434e38f7e3eeaac0de4d3325b93a924e441153ce"
},
"original_inference_code_commit": "a6a286f",
"procedural_format_code_commit": "dd1ac6e3b8bc2574b164680040b81703a1f3696f",
"normalization_sha256": "c96503622619f63528a12ddcb28b19d4beddff7d94522d11b6a83dabad654f43",
"original_pipeline_settings_hash": "1cbe629c2f1aef34160f2482e398fba26d131e90c582fff2f767158560a90916",
"release_format_hash": "731fb1dee60a16c4a665fa556cacb5266d0aa303366b3741cd0a7b4b8298b8fc",
"prompt_format_id": "humaneness-general-script-v2-20261011",
"machine_generated_not_human_verified": true,
"canonical_mixed_reference_z_and_caption_speech_reference_z_are_separate": true,
"raw_source_archives_unchanged": true
}Download the full annotation JSON · Voice caption weights/code · Burst caption weights/code · Humaneness Ears Medium